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Data compression in medical implants
Pål Anders Floor
Interventional Center,
Oslo University Hospital
Outline
• Removal of temporal correlation: Differential pulse code modulation (DPCM)
• Example: ECG sinal processing with DPCM
• Exploring inter-sensor correlation: Distributed quantization (DQ)
• Example: Combining DPCM and DQ
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Measuring physical processes
• Signals resulting from measurement of physical phenomenon are usually correlated
• Correlation in time: Compression with DPCM to reduce power consumption/rate
• Several sensors: exploit inter-sensor correlation with Distributed Quantization to reduce power/rate
• Combine schemes to exploit both temporal and inter-sensor correlation
• For implants: Crucial with low power consumption
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Temporal correlation (one sensor)
• Correlation is redundant -> can be removed at encoder and added at decoder
• Remove redundancy (compression) prior to transmission -> reduced power consumption
• Compression scheme must be of low complexity to be effective
• DPCM: Removes correlation at low complexity
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DPCM
• Exploits correlation by predicting current signal sample based on P previous samples
• Communicate prediction error• Predictor: P’th order FIR filter
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DPCM cont’d
• Predictor coefficients determined from relevant data.• Stationary signal: Solve Yule-Walker equations:
• - coeficients. - estimated correlation matrix from relevant data
• Non-stationary signal: Adapt coefficients with Levinson Recursion
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[ ]TdcR 001ˆ 2σ=
c R̂
DPCM and ECG cont’d• Power reduced ≈ 280 times with 2’nd order predictor• Quantize prediction error instead of original!• Low complexity:
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With a good feedback channel
• Predictor can be moved to receiver (CU) [2]:
• Very simple encoder
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Multi-user schemes
• Correlation between different sensors are exploited
• Can potentially achieve lower power consumtion per sensor
• Joint compression of several sources: Distributed Quantization
• Distributed: Since cooperation between implants is difficult
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2-node sensor network
• Orthogonal transmission
• Best possible encoders and decoders?• Optimal performance known for Gaussian case
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GSN cont’d
• Correlation must be very large to gain significantly from multiuser schemes
• Low correlation: Do not bother!
• High correlation: Worth exploring
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Distributed Quantization
• Encoders: Co-optimized scalar quantizers [3]
• : Re-use of quantizer indices -> Better resolution for given number of bits
]1,95.0[∈xρ
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Conclusion• DPCM gives significant compression of
correlated signals at low complexity• Multiuser schemes only gives compression gain
at high inter-sensor correlation• Combining DPCM and DQ: small number of bits
per sensor achieved• Most potential lies in DPCM coding for each
sensor
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References• [1]: S.M. Lalaleddine, C. G. Hutchens, R. D. Strattan, and W. A. Coberly. “ECG data
compression techniques – a unified approach”. IEEE Transactions on Biomedical Engineering., vol. 37, no. 4, pp. 329‐342. April 1990.
• [2]: T. A. Ramstad, “Simple and reliable power image communication based on dpcm and multiple refinements through feedback,” in 3rd International Symposium on Communications, Control and Signal Processing. IEEE, Mar. 2008.
• [3]: N. Wernersson, J. Karlsson, and M. Skoglund, “Distributed quantization over noisy channels,” IEEE Transactions on Communications., vol. 57, no.6, pp. 1693‐1700, June 2009.
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